Instructions to use tngtech/DeepSeek-R1T-Chimera with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tngtech/DeepSeek-R1T-Chimera with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="tngtech/DeepSeek-R1T-Chimera", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("tngtech/DeepSeek-R1T-Chimera", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("tngtech/DeepSeek-R1T-Chimera", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use tngtech/DeepSeek-R1T-Chimera with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tngtech/DeepSeek-R1T-Chimera"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tngtech/DeepSeek-R1T-Chimera",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/tngtech/DeepSeek-R1T-Chimera

SGLang

How to use tngtech/DeepSeek-R1T-Chimera with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "tngtech/DeepSeek-R1T-Chimera" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tngtech/DeepSeek-R1T-Chimera",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "tngtech/DeepSeek-R1T-Chimera" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tngtech/DeepSeek-R1T-Chimera",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use tngtech/DeepSeek-R1T-Chimera with Docker Model Runner:
```
docker model run hf.co/tngtech/DeepSeek-R1T-Chimera
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

DeepSeek-R1T-Chimera

Model merge of DeepSeek-R1 and DeepSeek-V3 (0324)

An open weights model combining the intelligence of R1 with the token efficiency of V3.

For details on the construction process and analyses of Chimera model variants, please read our paper.

Paper on arXiV | Announcement on X | LinkedIn post | Try it on OpenRouter

Update: we released R1T2-Chimera that is both faster and smarter than R1.

Model Details

Architecture: DeepSeek-MoE Transformer-based language model
Combination Method: Merged model weights from DeepSeek-R1 and DeepSeek-V3 (0324)
Release Date: 2025-04-27

Use, Out-of-scope Use, Limitations, Risks, Recommendations et al

Regarding R1T Chimera, we ask you to follow the careful guidelines that Microsoft has created for their "MAI-DS-R1" DeepSeek-based model.

These guidelines are available here on Hugging Face.

Contact

Email: research@tngtech.com
X.com: @tngtech

Citation

@misc{tng_technology_consulting_gmbh_2025,
    author       = { TNG Technology Consulting GmbH },
    title        = { DeepSeek-R1T-Chimera },
    year         = 2025,
    month        = {April},
    url          = { https://huggingface.co/tngtech/DeepSeek-R1T-Chimera },
    doi          = { 10.57967/hf/5330 },
    publisher    = { Hugging Face }
}